The present invention relates to methods for classifying tissue in magnetic resonance images (MRI), and more particularly, to a method for classifying and quantifying leukoaraiosis in the MRI image of a brain.
White matter hyperintensity (leukoaraiosis) on magnetic resonance imaging (MRI) of the brain of elderly persons is suspected to be a direct manifestation of microvascular ischemic injury in the distribution of the penetrating arteriolar vessels. A number of clinical studies have linked leukoaraiosis with cognitive impairment in the elderly, such as Alzheimer's disease and other forms of dementia. It is, therefore, desirable to be able to quantify the amount of leukoaraiosis in the brain. However, because leukoraiosis tissue is relatively small and is generally spatially non-contiguous, accurate quantification of leukoaraiosis tissue is difficult, particularly in clinical settings.
Most clinical research studies of leukoaraiosis volume employ semi-quantitative methods of viewer ranking. These methods are not automated and the results are therefore qualitative, depending significantly on the analysis of the viewer. These methods, therefore, do not provide reproducible quantitative results, and are insufficiently accurate for clinical research of aging and dementia.
Various methods of computer aided or automated methods of image segmentation and quantification of brain MRI are also known. Existing methods are capable of measuring, for example, global brain and cerebral spinal fluid (CSF) volume with high reproducibility. While acceptable when applied to these types of tissues existing methods of automated image segmentation have proved insufficiently accurate and reproducible when applied to small, spatially non-contiguous tissues, and do not provide sufficiently accurate results when applied to leukoaraiosis.
Another prior art method for classifying and quantifying brain tissue is multi-spectral segmentation. Multi-spectral segmentation algorithms are commonly used to segment and classify MS plaques, which, like leukoaraiosis, are most often located in the white matter, typically have elevated T2 signal with respect to adjacent normal brain tissue, and assume an anatomic configuration either of spatially distributed discrete foci or confluent areas of elevated signal in the periventricular white matter. In multi-spectral segmentation algorithms, two or more spatially registered MR image volumes with different contrast properties are used to define a feature-space. The images are segmented into different tissue classes based on the principle that specific tissues form clusters in feature-space. While such algorithms are effective in many situations, there are a number of disadvantages associated with these methods, particularly when applied to clinical analyses. First, to apply a multi-spectral algorithm, multiple sets of data are required, and therefore a relatively long scan time is needed. Alignment of large sets of data for registration purposes is often difficult, and can result in inaccurate results. Furthermore, due to the long acquisition time, one or more of the interleaved acquisitions is frequently out of registration with the others due to patient head motion during the acquisition, particularly when applied to the elderly or those suffering with dementia. Under these circumstances, the results of the scan may be entirely unusable. Alternatively, the results may be blurry but “usable”, again resulting in an inaccurate result.
A further disadvantage of multi-spectral segmentation algorithms is that such algorithms require supervision. Supervised algorithms require a trained operator to manually identify training sets of the major tissue classes of interest, for example CSF, brain, and leukoaraiosis lesion for each new set of images. The final result of a supervised classifier is highly dependent on operator-defined tissue classification input values which are unique to each data set. Small differences in operator judgment about the training dataset(s) may produce wide variation in results.
There remains a need, therefore, for an accurate, reproducible, and automatic method for measuring leukoaraiosis and whole brain volume in elderly subjects.